Effects of in-season short-term aerobic and high-intensity interval training program on repeated sprint ability and jump performance in handball players
Bibliographic record
Abstract
BACKGROUND: This study examined the effects of a 7-week in-season aerobic and high-intensity interval-training program on performance tests linked to successful handball play (e.g., repeated sprint and jumping ability). METHODS: Thirty participants (age 17.0±1.2 years, body mass 81.1±3.4 kg, height 1.82±0.07 m) performed a Yo-Yo Intermittent Recovery Test level 1 (Yo-Yo IR1), a squat (SJ) and a Countermovement Jump Test (CMJ), as well as a repeated Sprint Ability Test (RSA). From this, maximal aerobic speed (MAS, reached at the end of the Yo-Yo IR1), jumping ability, best time in a single sprint trial (RSAbest), total time (RSATT) and the performance decrement (RSAdec) during all sprints were calculated. Later, subjects were randomly assigned to a control group (CG; N.=15) performing their normal training schedule (5 weekly sessions of ~90 minutes of handball training) or an experimental group (EG; N.=15). The EG performed two 30 min sessions per week of high-intensity aerobic exercises at 100-130% of MAS in addition to their normal training schedule. RESULTS: A significant improvement in MAS (d=4.1), RSAbest (d=1.9), RSATT (d=1.5) and RSAdec (d=2.3) after the training period was demonstrated. Also, significant interaction effects (time x group) were found for all parameters as the EG significantly improved performances in all tests after training. The greatest interaction effects were observed in MAS (η2=0.811) and CMJ (η2=0.759). No relevant changes in test performances were found in the CG (mean d=-0.02). CONCLUSIONS: These results indicate that individually speed-controlled aerobic and interval training is effective for improving specific handball performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".